Analysis of EEG signals using multiresolution wavelet analysis and its extensions

The paper deals with the problem of developing tools for studying complex signals recorded in various experimental research. Considering the non-stationary nature of many processes in nature, it is important to apply and improve methods for analyzing the structure of experimental processes in the dynamics of systems with time-varied characteristics. One of the most popular approaches is wavelet analysis and methods that use decomposition in the basis of wavelet functions as the main or intermediate stage of analysis. In this study, various versions of extended multiresolution wavelet analysis (MWA) were tested, aimed at improving the quality of diagnostics of complex oscillations and their changes when the operating conditions of the system change. The characterization of differences between various group using the kurtosis and skewness of the probability distribution of the wavelet coefficients of decompositions improves the diagnosis of age distinctions compared to the approach based on the standard deviations.